Although numerous image warping schemes have been proposed, they are mostly designed for monochrome images. This means that on warping color images, these methods should be applied to each channel independently (e.g., [1]}) or to the luminance channel only (e.g.[2]}), without exploiting the cross-channel correlation. The only exception is end-to-end CNNs that take RGB images as input. In this situation, the color correlation is captured during the training process, but the learned correlation is CNN specific rather than being general. In contrast, cross-channel correlation is thoroughly studied for Image Demosaicking, which recovers the full RGB images from subsampled color channels, with only one primary color component available at each pixel [3]}. Although each channel can be recovered by merely using the intra-channel interpolation, it is commonly known that inter-channel correlation information can significantly improve the demosaicking accuracy.
